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Exploring Ethnic and Racial Discrimination with Carpooling Data

Copyright: Dariusz Sankowski

Decades of social science research provide plenty of evidence on ethnic and racial discrimination in various areas of society based on ethnographic work and analysis of traditional data sources. Online markets offer a new perspective to study the diverse settings in which ethnic discrimination can occur and provide new channels to test assumptions about why and how members of ethnic or racial groups are being discriminated against.

In a new study, Jasper Dag Tjaden, Carsten Schwemmer and Menusch Khadjavi looked at carpooling, a huge market that presents real competition to trains and buses in the medium to long distance segment in Europe. The unique setting of this application compared to others is that the driver – who offers a ride – and the customer – who selects the ride – spend time together in a car. This means that carpooling is not only an economic, but a social market with direct interaction.

Their results indicate a substantial ethnic discrimination in Germany’s carpooling market. Drivers with Arab/Turkish/Persian sounding names obtain less demand (on average 13% fewer clicks on their offer) compared to German drivers. The average Arab/Turkish/Persian driver in the analysis would have to offer his ride 4.20 € cheaper than the average German driver to achieve the same demand, a discriminatory price premium that is equivalent to 32% of the price for an average ride. These strong effects are surprising because customers in this market tend to be young and more urban than the general population – attributes that could contribute to reduced discrimination.

However, the authors also found that ethnic disparities decrease depending on the level of relevant information that is available about the driver. Higher user ratings, a higher number of ratings, and information on driver experience decrease ethnic discrimination substantially. In fact, ethnic disparities seem to disappear entirely for the highest rated drivers. This shows that stereotypes regarding particular ethnic groups become more salient and active when other information that could signal trust is scarce. In other words, discrimination is more pervasive in information-scarce environments. Consumers appear to use the name origin as a signal for other relevant information that is otherwise not available.

These results underscore the importance of a general discussion about anti-discrimination legislation in the Internet age. It is possible that due to one-to-one communication in online markets, discrimination goes largely undetected and unsanctioned. Moreover, the findings suggest that the type and level of information provided matters for the degree of discrimination, providing a useful leverage point for policymakers. Information is often more malleable to policy than deep-rooted prejudice. In cases where adding context information is not possible, another strategy is to remove the information or signal (ethnic cue) that induces unequal treatment of some users, i.e. the name. Without the name it is harder to assign (ethnic) group membership, and thus harder for stereotypes to be activated.